What is data? How to define data from different viewpoints? What are tools in Data Technology & what to use when? How to apply Data Governance & build Data Strategy? And finally, how every aspect mentioned above fits together in business & technology ecosystem? Data at the fingertips of almost every professional can be truly transformational. So building Data-Driven Culture is the most challenging yet the most rewarding aspect. And to create a Data-Driven Culture, first and foremost thing is to make every employee, every professional data literate.
Today, we are siloed in how we think about IoT. We develop solutions for the sake of technology and continue to think in small incremental steps about the data we are collecting. It’s relatively easy and cheap to deploy a connected sensor and collect data, but it’s the easy way out and everyone is doing it. The industry is missing a critical link: the marketplace for IoT to use the data collectively and build an ecosystem for distributed monetization of data. This is where AI comes in. The convergence of AI and IoT can change this by creating a connected system of things that can be used in everyday life.
Companies are under increasing pressure to constantly innovate, often guided by a digital transformation or corporate innovation charter that is mandated by the C-suite, supported by middle management guidance and executed by grassroots “intra-preneurs.” This mounting pressure can serve as both a blessing and a curse for survival. Change agents strive to not only brainstorm the next big idea that will push the company into a new era of technology revolution, but also simultaneously hide their efforts from colleagues and other departments in order to get the glory of being the smartest person in the room.
Cloud services now provide the capability to both store and compute vast amounts of data. As both public and private clouds have quickly become a business necessity with the explosion in the availability of data in recent years, the evolution of Datacenter depends on a well-designed cloud-based architecture that is capable of flawless delivery, and must include five major processes - Visualize, Consolidate, Integrate, Automate, Federate. Indeed, cloud computing is widely leveraged across a variety of problem domains ranging from movie recommendation systems to unraveling the mysteries of the universe.
One of the main problems with organizations attempting digital transformation is an embedded complexity in their processes. This complexity has usually arisen from being product-focused rather than customer-focused. While tackling the process innovation, it is not something that should be delayed. With two-speed IT, one now has to introduce a whole new IT model for the agile development, which includes more new processes, instead of striving for simplicity. The short-term goal of IT business units should be to move to the agile philosophy, which is a milestone on the roadmap to continuous delivery and implementing DevOps.
IIoT projects are change agents. They help create new digital ecosystems and value chains. Participation in these value chains can be the difference between success and failure. How will IIoT drive more sales? This question stalls many industrial IoT projects.How will IIoT create more shareholder value? Now that is a much better question. Successful IIoT projects are customer and engagement focused, creating value through digital transformation. Well-managed IIoT projects can help transform customer, employee and partner engagements. To accomplish this, provide a clear IIoT plan that includes relationship, retention, and revenue value creation.
What is data science? Why it is important? What is the difference between Artificial Intelligence, Data Science, and Machine Learning and Deep Learning? Data Science is an amalgamation of many other fields like mathematics, technology and domain. It has its own concepts, process and tools. It’s really tough to know each and everything related to the subject unless you have really worked on complex data science problems in the industry for a couple of years. You can learn the data science concepts like types of learning and when to use which kind of learning algorithms?
Learn the process of defining intents and entities and building a dialog flow for your chatbot to respond to customer queries. You define an intent for each type of user request you want your application to support. You list the possible values for each entity and synonyms that users might enter. You will learn how to enable Speech to Text and Text to Speech services for easy interaction with the Android app. Also, track the app’s usage metrics through Mobile Analytics service.
One of the most obvious developments that have taken place in the world is in the field of medical science. Radiology has allowed medical professionals to pinpoint the causes of symptoms of a patient. Reconstructive surgery has enabled breast cancer survivors to have the choice of rebuilding the look and shape of their breasts.
Robotics, Machine Learning (ML) and AI is starting to dominate the enterprise, service providers and consumer worlds for decades to come. We are entering to perhaps another major showdown for use of technology using Artificial Intelligence and Robotics with massive amount of sensors for years to come and I predict number of sensors in entire world economy will exceed 1T by end of 2030 time-frame and this will generate level of innovation and growth in enterprises, consumers and governments which we had not seen except for industrial revolution in 20th century.
Machine learning has been redefining how even the basics of operational tasks are done across industries. The financial industry is no different. While some of the applications of machine learning in finance are clearly visible to us - like mobile banking apps and chatbots, the technology is now being gradually used for drawing out accurate historical data of customers and predicting their future needs as well.
The eCommerce industry is growing by manifolds across the world. From what started as a few stores that enabled online shopping; today, the smallest of brands are able to take their products online and market them to a large consumer base. Call it the ease of technology and the ability to use data, almost every eCommerce store is able to capture a segment of the consumer market - despite the rising competition.
RPA software has proven to reliably reduce costs by removing manual work from various business workflows and processes. But is RPA adoption by all enterprises need to automate their business processes? What else does process automation have in store other than RPA? To answer these questions, it helps to understand where RPA technologies came from and at what capabilities they now offer. Using machine-learning platforms to also incorporate new information gathered from background collection of workflow exceptions is the most practical next step to achieving full automation. We have far to go before RPA fulfills its “robotic” mission of removing the human element.
You should know about Artificial Intelligence and Machine Learning in the healthcare industry and how it will impact our future. These technologies WILL dramatically change the way we work in healthcare. As the use of Machine Learning grows in healthcare, continue to obsess over the privacy of your customer data. Making “cool” innovations in Artificial Intelligence or Machine Learning won’t work if not coupled with a relentless pursuit to serve the customer. These endeavors are expensive, so spend your IT budget wisely, ensuring new innovation creates true value and is easy for the end user.
AI-based technology will fundamentally change economies, politics, the planet, and indeed humanity. Even today we are only just beginning to see some of these changes come to fruition. For better or for worse, society will be permanently altered due to artificial intelligence. Just think of the dramatic changes we’ve witnessed just in our own lives as the age of the Internet has disrupted the landscape. Given the dramatic pace of innovation today, one can’t help but wonder what humanity might look like in a few decades as compared to today. How will we, as a society, fare in the brave new world of tomorrow?
ALL businesses are in need of digitization, with the vast majority eagerly trying to to find a digitization strategy and trusted partners to help them get there quickly. The time for digitization is now, and not making this a core part of your business's strategy in 2018 is not just dangerous, it is fatal. The good news is, there are a lot of great partners out there to help you along, picking the right one is just another part of your Digital Transformation Story. HOW you tell that story, while understanding exceedingly changing industry environments, the data realities of the current condition today, and the human/machine capital needed to drive initiatives, is immensely valuable.
As the amount of structured and unstructured data explodes, the financial sector is realizing the necessity of harnessing and analyzing that data in the fastest, most effective way possible in order to stay competitive. A revolution can be defined as a fundamental change in an organizational structure that takes place in a relatively short period of time when people “revolt” against the current order. Currently, the financial sector is making a massive shift towards big data and machine learning technology and applied solutions. Here are five signs that this is the beginning of a revolution in finance
Watching the bitcoin phenomenon is a bit like watching the three-decade decline of the internet from a playspace for the counterculture to one for venture capitalists. We thought the net would break the monopoly of top-down, corporate media. But as business interests took over it has become primarily a delivery system for streaming television to consumers, and consumer data to advertisers. Likewise, Bitcoin was intended to break the monopoly of the banking system over central currency and credit. But, in the end, it will turn into just another platform for the big banks to do the same old extraction they always have. Here’s how.
The introduction of PFE is the beginning of a revolution in relations between the bank and its clients. The insights that flow from it will primarily build new value for users, intrinsically bonding them with the bank. Along with the development of artificial intelligence algorithms, more and more sophisticated ways will emerge that will pre-empt their clients' behaviour and support them in everyday life. There will be ideas for dynamic adjustment of the bank's communication to key moments in the life of the user, providing summaries after international trips, gift expenses, car running costs since the last refueling or a summary of taxi expenses.
Learn the process of building a predictive machine learning model, deploying it as an API to be used in applications, testing the model and retraining the model with feedback data. In this post, the famous Iris flower data set is used for creating a machine learning model to classify species of flowers. In the terminology of machine learning, classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. Following the steps, you will deploy your model as an API, test it and retrain by creating a feedback data connection.
Fraud analytics can identify the current behaviour and help in fraud detection whereas applying this knowledge in a model of predictive analytics can help in fraud prevention. Since tasks like data extraction and pre-processing are of paramount importance, we would need data scientists who possess not only a technical knowhow but more importantly patience, perseverance, critical thinking, and domain understanding. In here, the imputation for missing values may not be required but reported for certain attributes. Even when required, it may not be as easy and straightforward as in the different problem statements, especially when a few indicators are about to raise a red flag.
Data ingestion and big data storage were the most foreign to marketing leaders. Understanding where each team sat in the organizations' data story, was incredibly powerful and seemed to inspire the accountability and permission for business leaders to engage in a more informed and strategic technology conversation with IT. CIOs and CMOs must share each other's mindset in how data plays a part in the organizations business strategy, and if this isn't the case, both will end up overspending and over allocating budget and talent in the quest to "be an analytical organization". Here are some items for marketing leaders to explore if all of this sounds familiar:
Artificial intelligence is an incredibly complicated concept for application testing. There aren’t that many products that offer real AI/machine learning functionality for app QA. Your best bet is to find a QA team that has in-house machine learning solutions or uses one of the tools that we mentioned and their alternatives. This way, your app testing needs will get the maximum coverage that they deserve. It’s also important to remember that traditional QA automation still works. You don’t have to jump on the AI bandwagon just because everyone is using it in their marketing nowadays.
Travel and tourism is on the rise globally. The industry now accounts for more than one-tenth of the world’s GDP. Interestingly, the target market is not only from developed nations but also from the emerging parts of the world that boast of increasing disposable incomes and a strong desire to explore cultures outside their own.